Multivariate factor analysis of heavy minerals concentrate from Athabasca oil sands tailings by X‐ray photoelectron spectroscopy
Bibliographic record
Abstract
Multivariate factor analysis of X‐ray photoelectron spectroscopy data acquired from Athabasca oil sands heavy minerals concentrate was used to identify the primary mineral components and their physical associations. Using large‐area spectroscopy, a principal components analysis (PCA) and Varimax rotation of the PCA spectral loadings matrix demonstrated that the most significant factors index the mineral chemistry by virtue of the within‐factor spectral correlations. Analysis of the Varimax rotated factor scores indicated the physical character of several mineral associations. Emphasis is placed on the high‐value materials, namely, zircon and the titanium‐bearing minerals. In spectral imaging mode, Varimax rotation in the spatial domain applied to a PCA noise‐reduced data reconstruction was used to render component images illustrating the spatial distribution of selected mineral chemistries. The component images also revealed evidence of surface species consistent with pyrite weathering. Data are supported with optical microscopy and energy‐dispersive X‐ray spectroscopy. Our work demonstrates the utility of multivariate spectroscopic techniques in the analysis of complex mineral chemistry. © 2013 National Research Council Canada and John Wiley & Sons Ltd.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".